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Updated: Jun 20, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Deep residual 2D convolutional neural network for cardiovascular disease classification.

Haneen A Elyamani¹, Mohammed A Salem², Farid Melgani³

¹Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, 44745, Egypt. hanen_yamany@science.suez.edu.eg.

Scientific Reports

|September 26, 2024

Summary

A novel deep learning model for electrocardiogram (ECG) analysis shows high accuracy in detecting cardiovascular diseases (CVD). This AI-driven approach enhances diagnostic efficiency, improving accessibility to cardiac care.

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Area of Science:

Cardiology
Artificial Intelligence
Medical Imaging Analysis

Background:

Cardiovascular disease (CVD) remains a significant global health issue.
Manual interpretation of electrocardiograms (ECGs) limits widespread diagnostic accessibility.
Automated ECG analysis offers potential for improved accuracy and efficiency.

Purpose of the Study:

To implement and evaluate a novel deep two-dimensional convolutional neural network (2D-CNN) for cardiac disorder detection using ECG data.
To assess the performance of the 2D-CNN across different classification complexities (2, 5, and 23 cardiovascular disease classes).

Main Methods:

A deep two-dimensional convolutional neural network (2D-CNN) was developed and applied to the PTB-XL dataset.
The model was trained and validated for classifying cardiovascular conditions into 2, 5, and 23 distinct classes.

Main Results:

The 2D-CNN achieved an Area Under the Curve (AUC) of 95% and 87.85% average accuracy for healthy/sick patient classification.
In a 5-class classification, the model reached an AUC of 93.46% and 89.87% average accuracy.
For 23-class classification, the model demonstrated an AUC of 92.18% and 96.88% accuracy, outperforming other methods on the same dataset.

Conclusions:

The developed 2D-CNN model shows strong performance in classifying various cardiovascular diseases from ECGs.
This AI-driven approach can assist healthcare professionals in clinical ECG analysis and computer-aided diagnosis.
The findings suggest a potential for enhanced accessibility and accuracy in cardiovascular care.